1D Average pooling

The 1D Average pooling block represents an average pooling operation. This block outputs a smaller tensor than its input, which means downstream blocks in your model will need fewer parameters and amount of computation; it also serves to control overfitting.

The 1D Average pooling block moves a pool (window) with a set size over the incoming data, computing the average in each specific window. How big steps the window takes is determined by the stride.

1D Average pooling
Figure 1. A 1D average pooling with a pool sized 2 and a stride of 2.

Average pooling blocks are inserted after one or more convolutional blocks; they help inner convolutional block receive information from a bigger portion of the original vector. If we see convolutional blocks as detectors of a specific feature, average pooling finds the “mean” value of that feature inside the pooling vector. Each channel (hence each feature) is treated separately.


Size: The length of the vector with which the average is computed.

Stride: Distance between the left edge of consecutive pooling windows.

Padding: Same results in padding the input such that the output has the same length as the original input. Valid means "no padding".

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